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Learning Descriptions from the Semantic Web. Gunnar Aastrand Grimnes Supervisors: Pete Edwards & Alun Preece [email protected] Away Day 30/4/2004. Introduction. Semantic incompleteness: Personal classifications Time/context dependent Granularity difference => Machine Learning

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learning descriptions from the semantic web

Learning Descriptions from the Semantic Web

Gunnar Aastrand Grimnes

Supervisors: Pete Edwards & Alun Preece

[email protected] Away Day

30/4/2004

introduction
Introduction
  • Semantic incompleteness:
    • Personal classifications
    • Time/context dependent
    • Granularity difference
  • => Machine Learning
    • Identify classes
    • Learn descriptions
    • Feed back into ontologies or use on the spot.
friend of a friend foaf
Friend of a Friend (FOAF)
  • “The Friend of a Friend (FOAF) project is about creating a Web of machine-readable homepages describing people, the links between them and the things they create and do.”
  • 8908 people – 1980 know at least one person.
  • 147527 triples – 201 namespaces and 1066 unique properties.
  • FOAF data was also enriched by mapping AKT to FOAF.
example profile
Example Profile

<foaf:Person>

<foaf:mbox rdf:resource=“mailto:[email protected]” />

<foaf:name>Gunnar AAstrand Grimnes</foaf:name>

<foaf:projectHomepage rdf:resource=“.../research/agentcities”/>

<foaf:groupHomepage rdf:resource=“.../research/agentsgroup” />

<foaf:depiction rdf:resource=“.../~ggrimnes/gfx/me.jpg” />

<foaf:interest rdf:resource=“http://www.w3.org/2001/sw/” />

<foaf:interest rdf:resource=“http://www.agentcities.net” />

<foaf:made rdf:resource=“.../research/AgentCities/GraniteNights” />

<contact:nearestAirport>

<airport:Airport rdf:about=“http://www.daml.org/airport?ABZ” />

</contact:nearestAirport>

example profile cont
Example Profile cont.

<foaf:knows>

<foaf:Person>

<foaf:mbox rdf:resource=“mailto:[email protected]” />

<rdfs:seeAlso rdf:resource=“http://martinmay.net/foaf.rdf”/>

</foaf:Person>

</foaf:knows>

<foaf:knows>

<foaf:Person>

<foaf:mbox rdf:resource=“mailto:[email protected]” />

</foaf:Person>

</foaf:knows>

<foaf:knows>

<foaf:Person foaf:name=“Sonja A Schramm”>

<foaf:mbox_sha1sum>83276f91273f2900cf0b6657b3708b736276ef81</foaf:mbox_sha1sum>

</foaf:Person>

</foaf:knows>

</foaf:Person>

<rdf:Description rdf:about=“”>

<wot:assurance rdf:resource=“foaf.rdf.asc” />

</rdf:Description>

foafnaut
Foafnaut

Fun!

&

Instant gratification!

pre processing foaf
Pre-processing FOAF
  • FOAF is far from heterogeneous:
    • Human errors, i.e. foaf:knows
    • Wrong namespace, i.e. rdf:seeAlso, (not rdfs)
    • foaf:knows resource vs. literal
    • No uniform way of specifying foaf:interest
  • Copy and Paste culture  islands using specific properties.
clustering
Clustering
  • Hierarchical Agglomerative Clustering

CSD

A3

AKT

?

Pete

Gunnar

Alun

Derek

Dave

distance metric
Distance Metric
  • Hamming distance was unsatisfactory.
  • Need to consider graph surrounding person.
  • Distance metric for comparison of conceptual graphs (Montes-y-Gómez et al., 2000)
  • Given two (sub)graphs, considers node and edge overlap.
similarity measure
Similarity Measure

Gc

  • Sc = Node overlap for graph G1 & G2 :

G2

G1

A

A

x

y

x

y

B

C

B

C

B

x

z

x

x

z

z

D

F

D

F/A

D

A

z

z

E

E

the ilp system aleph
The ILP System Aleph
  • Evaluating new classifications:
    • Qualitative assessment
    • Real life correspondence
    • Utility
  • Results were better when learned without foaf:knows.
  • Sloooow
    • Weeks to learn descriptions of clusters for only 10% of full data.
    • Identify “interesting clusters” based on distance:
results
Results

member(A) :-

dc___creator(B,A),

dc___title(B,”Managing Reference: Ensuring Referential Integrity of Ontologies for the Semantic Web”).

member(A) :-

contact___nearestAirport(A,”http://www.daml.org/airport?ABZ”).

member(A) :-

foaf___groupHomepage(A,”http://www.aktors.org”).

member(A) :-

trust___trustsHighly(B,A).

member(A) :-

dc___creator(B,A), dc___format(B,”application/postscript”).

conclusion
Conclusion
  • Learned Prolog descriptions converted into OWL class descriptions, or rules expressed in SWRL.
  • Need evaluation function – currently relies on visual inspection.
  • Applications of good descriptions:
    • Ontology evaluation / engineering guidance.
    • Personalisation?
slide14
More:
  • Two papers:
    • Learning from Semantic Flora and Fauna

Accepted forSemantic Web Personalisationworkshop atAAAI’04.

    • Learning Meta-Descriptions of the FOAF Network

Submitted toThe International Semantic Web Conference’04.

  • Questions?
interesting cluster measure
Interesting cluster measure

distc - distance between children or current node.

dista & distb - distance between each pair of grand-children.

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